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检索条件"机构=Dept. of Computer Engineering and Information Technology"

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Contribution of Algorithm Visualizations to Students' Learning Skills: A Pedagogical Approach 12

Contribution of Algorithm Visualizations to Students' Learni...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kheder, Harem Ali Varol, Asaf College of Engineering and Computer Sciences Lebanese French University Dept. of Computer Engineering Erbil Iraq College of Engineering and Computer Science The University of Tennessee at Chattanooga Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350330366

The Domain Algorithm visualizations are multimedia-based representations of algorithms that help students learn how they operate and behave. Algorithm visualizations provide numerous benefits to instructors and students, including an improved understanding of difficult algorithms in programming. By stripping down the complexities of algorithms and presenting them in simplified visual ways, students can obtain a more thorough knowledge of how they perform. Instructional approaches, such as guided instruction or discovery learning, are critical to the pedagogical effectiveness of algorithm representations. Guided instructions can considerably boost learners' overall performance in problem-solving activities that incorporate visualizations. At the same time, discovery education promotes exploration and experimentation, yet it may result in longer learning times and increased mental demands. A combination of these approaches is required for optimal effectiveness of instruction. In this paper, we investigate how the pedagogical effectiveness of algorithm visualizations should be determined by a variety of elements, including student characteristics, visual and cognitive processing ability, algorithm representation, and instructional context based on a literature review and interviews with field experts, and a bunch of recommendations is provided that has witnessed by authors during their long teaching periods. © 2024 IEEE.

关键词： Students

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Kumar, Ch Sree Singh, Akhilendra Pratap Reddy, K Hemant K National Institute of Technology Meghalaya Dept. of Computer Science Meghalaya India VIT-AP Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350369175

Distributed Ledger technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes. © 2024 IEEE.

关键词： Distributed ledger

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm 4

SARCASAM Analysis in Social Networks Using Deep Learning Alg...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Priya, M. Kumar, Vijaya K Vennila, P. Prasanna, M.A. Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Dept. of Computer Science and Engineering Women's Engineering College Puducherry605008 India Dept. of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy621112 India

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one's own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers' opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). © 2025 The Authors.

关键词： Tweets

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AI-Based Home Automation Using Voice Recognition and Biometric Finger Print Authentication 4

AI-Based Home Automation Using Voice Recognition and Biometr...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Pydala, Bhasha Bhargavi, M. Vyshnavi, Balaji Krishna, Machavaram Gopi Kumar, Aravadasari Lenin Bindu, K Hima dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College dept. of Information Technology Tirupati India

ISBN: (纸本)9798350385793

The proposed home automation system introduces a cutting-edge solution that harnesses the power of Artificial Intelligence (AI) to upgrade residential spaces into smart, dynamic, and user-friendly environments. With a simple vocal command, the AI system swiftly and precisely executes tasks. The existing home automation system lacks in authentication so to ensure security and personalization, the proposed system utilizes fingerprint module integrated with a microcontroller like Arduino Uno and Bluetooth module. It interfaces seamlessly with a fingerprint sensor, providing reliable user authentication. This ensures that the smart home system responds only to authorized users, enhancing overall safety. The implementation includes essential components such as Arduino Uno, Bluetooth modules, regulated power supply and fingerprint module. This innovative system showcases the potential of integrating AI into homes, making them not only intelligent but also adaptable to the different requirements of each resident. With speech recognition and fingerprint authentication, this system offers a glimpse into a future where homes are not just structures but responsive companions that enhance the quality of life. © 2024 IEEE.

关键词： Bluetooth

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Navigating the Horizon Towards 6G Wireless Communication Networks

Navigating the Horizon Towards 6G Wireless Communication Net...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Akinwunmi, Damilare Uche, Obinna Sokunbi, Onevemi Akinwumi, Hannah Awodele, Oludele Ajaegbu, Chigozirim Babcock University Dept. of Computer Science Ilishan-Remo Nigeria Babcock University Dept. of Information Technology Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

Sixth generation (6G) wireless communication is the next frontier. Researchers and business professionals are already focusing on the sixth generation of wireless technology, even though 5G is still being deployed globally. What can we anticipate from 6G, then? It's anticipated to be a dramatic advancement over 5G, providing previously unheard-of speeds, extremely low latency, and connectivity that extends well beyond laptops and smartphones. In our future, almost everything will be effortlessly connected;consider augmented reality, IoT gadgets, and more. Artificial intelligence integration is a crucial component of 6G. More effective resource management, improved user experiences, and network performance optimisation are all anticipated to be greatly aided by AI. The integration of AI with wireless communication is predicted to bring in a new age of intelligent, adaptable, and responsive networks. The pathway towards 6G wireless communication networks is thoroughly examined in this study, with an emphasis on possible innovations, difficulties, and game-changing potential. Also, this paper proposes an AI-driven framework for 6G networks, focusing on optimizing resource management, self-healing, and network security. It aims to improve real-time decision-making, reduce latency, and enhance fault recovery using machine learning. Simulations validate the framework's effectiveness and offer scalable solutions for 6G deployment. © 2024 IEEE.

关键词： 5G mobile communication systems

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An Intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques 4

An Intelligent Forecasting Classification Model for Traffic ...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kishor, Bodla Tiwari, Rajesh Patra, Raj Kumar Pinjakar, Latika Singh, Mahesh Cmr Engineering College Dept. of Computer Science and Engineering Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Hyderabad India Symbiosis Institute of Technology Dept. of Computer Science and Engineering Nagpur India Shri Shankaracharya Technical Campus Dept. of Eee Durg Bhilai India

ISBN: (纸本)9798350343670

In addition to taking care of the normal citizens, Road Traffic protection is also one of the main issue for corporations governing the transportation. We use data mining techniques to get the hidden matter from the database. The applications of Data mining is there in various fields such as fraud detection, marketing and many more. Many countries were affected due to many decisions that were taken by the governments one such issue is globalization. This has brought some drastically changes in the intake level and the financial related activities and this has given a boost to transportation department. Understanding the importance of the street protection the authorities are putting effort to know the reasons behind the street accidents. The increase in the street injuries made it difficult to determine the limitations of the street injuries. This paper explains the street injuries frequency from analysing the database. This work is about 95% efficient. © 2024 IEEE.

关键词： classification data mining machine learning traffic analysis Transportation models

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Kumari, Sweety Kumar, Biresh Thakur, Kanika Gaurav, Kumar Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9798350385199

An innovative data structure called a 'dynamic tree' has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of many algorithms. In this work, I particularly address the 'Maximum Flow method,' a crucial subtopic in the larger field of dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of the algorithm. This work reduces the time complexity of network flow problems, leading to more effective solutions. The findings have potential implications in a variety of computing and real-world scenarios. © 2024 IEEE.

关键词： Ancestor Cut Evert Find Key Link Root Top tree

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Pseudo-Holographic Display of Plant Models based on Pepper's Ghost Illusion 13

Pseudo-Holographic Display of Plant Models based on Pepper's...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Umezu, Nobuyuki Iijima, Satoshi Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798350373332

We propose a new display method for visualizing flower models using a pseudo-holographic technique known as the Pepper's ghost illusion. This classic optical illusion technique creates the appearance of floating objects by reflecting them off a transparent panel, thereby producing a convincing visual effect that simulates three-dimensionality. In our approach, we utilized this illusion to display six distinct flower models obtained from a comprehensive photogrammetry archive of various plant species. These models were rendered to overlay onto a transparent glass vase, enhancing the realism of quality. To facilitate user interaction with the displayed models, we incorporated a dept. sensor (Intel D415) to track the user's hand position. A user study revealed that participants were notably attracted to the display, indicating successful capture of their attention. Feedback, however, suggested that the system requires enhancements in usability and dept. perception to improve the overall user experience. Our future work includes addressing these limitations by developing a larger display setup that could accommodate more complex interactions and by integrating motion-parallax techniques to enhance the three-dimensional view. This could be implemented using head-tracking technology, further advancing the effectiveness of the pseudo-holographic display. © 2024 IEEE.

关键词： Holographic displays

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Human Abnormal Activity Prediction of Long Short-Term Memory using CNNs and RNNs in Deep Learning 9

Human Abnormal Activity Prediction of Long Short-Term Memory...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Prathima, Ch. Suhel, Shaik Muhammed Rajeti, Roje Spandana Naik, Mude Sai Kumar Chaithanya, Singamsetty Krishna Reddy, Nallabathini Prabhakar Dept. of Ds A.Rangampet Tirupati India Sree Vidyanikethan Engineering College Dept. of Information Technology A.Rangampet Tirupati India JNTUGV-College of Engineering Department of Information Technology Vizianagaram India

ISBN: (纸本)9798350365092

This research presents a unique method of predicting aberrant activity in humans by integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN). By using the advantages of CNNs for frame-by-frame spatial analysis and LSTM networks for temporal dependency modeling inside the frame sequence, our hybrid model seeks to efficiently capture both spatial and temporal characteristics in video sequences. Combining these two designs, we get a strong model that can identify complex patterns suggestive of anomalous human behavior. The CNN LSTM hybrid model that has been suggested performs better in terms of accuracy and resilience when predicting aberrant behaviors than conventional approaches and standalone models, as shown by experimental validation on a variety of benchmark datasets. The integration of both spatial and temporal information advances our knowledge of the dynamics at play in the identification of aberrant behavior. Furthermore, our approach is flexible enough to be used to real-world surveillance situations, demonstrating encouraging outcomes in scenarios with different camera viewpoints and environmental circumstances. The use of CNN and LSTM not only improves prediction accuracy but also makes the model more interpretable by shedding light on the spatiotemporal characteristics of aberrant behaviors. To sum up, this study advances the field of intelligent video analysis by providing a useful tool for predicting anomalous human behavior. This tool has potential uses in security monitoring, public safety, and anomaly detection systems. © 2024 IEEE.

关键词： Long short-term memory

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